MOTIVATION: Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity. RESULTS: We present an algorithm merging microarray datasets on the basis of co-expression alone, without any requirement for orthology information to affiliate genes. Combining existing methods such as co-inertia analysis, back-transformation, Hungarian matching and majority voting in an iterative non-greedy hill-climbing approach, it affiliates arrays and genes at the same time, maximizing the co-structure between the datasets. To introduce the method, we demonstrate its performance on two closely and two distantly related datasets of different experimental context and produced on different platforms. Each pair stems from two different species. The resulting cross-species dynamic Bayesian gene networks improve on the networks inferred from each dataset alone by yielding more significant network motifs, as well as more of the interactions already recorded in KEGG and other databases. Also, it is shown that our algorithm converges on the optimal number of nodes for network inference. Being readily extendable to more than two datasets, it provides the opportunity to infer extensive gene regulatory networks. AVAILABILITY AND IMPLEMENTATION: Source code (MATLAB and R) freely available for download at http://www.mchips.org/supplements/moghaddasi_source.tgz.

MOTIVATION: The functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists. RESULTS: To facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas. AVAILABILITY: The M-CHiPS software is accessible for collaborators at http://www.mchips.org

MOTIVATION: Microarray technology provides access to expression levels of thousands of genes at once, producing large amounts of data. These datasets are valuable only if they are annotated by sufficiently detailed experiment descriptions. However, in many databases a substantial number of these annotations is in free-text format and not readily accessible to computer-aided analysis. RESULTS: The Multi-Conditional Hybridization Intensity Processing System (M-CHIPS), a data warehousing concept, focuses on providing both structure and algorithms suitable for statistical analysis of a microarray database's entire contents including the experiment annotations. It addresses the rapid growth of the amount of hybridization data, more detailed experimental descriptions, and new kinds of experiments in the future. We have developed a storage concept, a particular instance of which is an organism-specific database. Although these databases may contain different ontologies of experiment annotations, they share the same structure and therefore can be accessed by the very same statistical algorithms. Experiment ontologies have not yet reached their final shape, and standards are reduced to minimal conventions that do not yet warrant extensive description. An ontology-independent structure enables updates of annotation hierarchies during normal database operation without altering the structure. AVAILABILITY AND SUPPLEMENTARY INFORMATION: http://www.dkfz.de/tbi/services/mchips

MOTIVATION: The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS: Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY: http://www.dkfz.de/tbi CONTACT: [email protected]